Integrated Computer-Aided Engineering - Volume 9, issue 1

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ISSN 1069-2509 (P)
ISSN 1875-8835 (E)

Impact Factor 2018: 3.667

The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality.

Abstract: The primary objective was to develop a fast learning dynamic controller for uncalibrated visual guidance of a robotic arm. A combination of neural networks learning with an evolutionary method allowed for the study of the interaction of the two techniques in a non-trivial real world application. The neural network controller learned the relationship between changes to the image coordinates, in two cameras, of the arm's end effector due to observed movements, and the motor commands that caused these movements, during its lifetime. This eliminated the need for calibration and made the controller robust to repositioning of the equipment. Many parameters…of the controller were evolved by an evolutionary algorithm but not the neural network weights. The aim was to produce a neural network that could rapidly learn the geometry of the arm space using the backpropagation (BP) weight training rule, rather than evolving the weights directly. This is the first time that such a combination of evolutionary neural computing research techniques have been used in the context of a robotic manipulator application. To reduce the time taken for the evolution to within practical limits a minimal simulation approach was used to evolve the learning parameters and the resulting networks were tested both on the simulator and on a physical robot arm in the real world.
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Abstract: We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration scheme of which follows a neural network learning like dynamic. It is adapted from the approach of collective learning in self-organizing maps with a more and more separation during time. We succesfully apply this approach to clustering real world data in psychotherapy research and VLSI-design. The advantages of the approach are shown which consist in a reduced communication overhead between the subpopulations preserving a non-vanishing information flow and an improved convergence rate resulting in decreasing computational costs.

Abstract: In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization…is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, both the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design.
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Abstract: Considering computational algorithms available in the literature, associated with supervised learning in feedforward neural networks, a wide range of distinct approaches can be identified. While the adjustment of the connection weights represents an omnipresent stage, the algorithms differ on three basic aspects: the technique chosen to determine the dimension of the multilayer neural network, the procedure adopted to specify the activation functions, and the kind of composition used to produce the output. Advanced learning algorithms should be developed to simultaneously treat all these aspects during learning, and an evolutionary learning algorithm with local search is proposed here. The essence of…this approach is a synergy between genetic algorithms and conjugate gradient optimization, operating on a hybrid neural network architecture. As a consequence, the final neural network is automatically generated, and is characterized to be dedicated and computationally parsimonious.
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Abstract: Evolutionary algorithms have been successfully applied to the design and training of neural networks, such as in optimization of network architecture, learning connection weights, and selecting training data. While most of existing evolutionary methods are focused on one of these aspects, we present in this paper an integrated approach that employs evolutionary mechanisms for the optimization of these components simultaneously. This approach is especially effective when evolving irregular, not-strictly-layered networks of heterogeneous neurons with variable receptive fields. The core of our method is the neural tree representation scheme combined with the Bayesian evolutionary learning framework. The generality and flexibility of…neural trees make it easy to express and modify complex neural architectures by means of standard crossover and mutation operators. The Bayesian evolutionary framework provides a theoretical foundation for finding compact neural networks using a small data set by principled exploitation of background knowledge available in the problem domain. Performance of the presented method is demonstrated on a suite of benchmark problems and compared with those of related methods.
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Abstract: This paper proposes an approach for the specification of human-machine dialogue for interactive process control applications supported by a tool called Ergo-Conceptor+. This approach is based on a formal modelling of the Human-Machine System (HMS) behaviour. This modelling make possible the deduction of the user requirements and then the identification of the User Interface (UI) objects. A formalism using Interpreted Petri Nets is proposed for modelling the Human-Machine dialogue. The formal aspect allows the validation of the specifications before going on to the generation of the interface.